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Databricks ML in Action

You're reading from   Databricks ML in Action Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781800564893
Length 280 pages
Edition 1st Edition
Languages
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Authors (4):
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Hayley Horn Hayley Horn
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Hayley Horn
Amanda Baker Amanda Baker
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Amanda Baker
Anastasia Prokaieva Anastasia Prokaieva
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Anastasia Prokaieva
Stephanie Rivera Stephanie Rivera
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Stephanie Rivera
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Toc

Table of Contents (13) Chapters Close

Preface 1. Part 1: Overview of the Databricks Unified Data Intelligence Platform FREE CHAPTER
2. Chapter 1: Getting Started and Lakehouse Concepts 3. Chapter 2: Designing Databricks: Day One 4. Chapter 3: Building the Bronze Layer 5. Part 2: Heavily Project Focused
6. Chapter 4: Getting to Know Your Data 7. Chapter 5: Feature Engineering on Databricks 8. Chapter 6: Tools for Model Training and Experimenting 9. Chapter 7: Productionizing ML on Databricks 10. Chapter 8: Monitoring, Evaluating, and More 11. Index 12. Other Books You May Enjoy

Answers

After putting thought into the questions, compare your answers to ours:

  1. We cannot answer this question, but we hope you learn something you can use in your career soon!
  2. The glass-box approach supports transparency by providing the code run for each run in the experiment and the best run, thus enabling reusability and reproducibility.
  3. Apache Iceberg, Apache Hudi, and Linux Foundation Delta Lake (an open source/unmanaged version of Delta).
  4. There are several. Here are a few:
    • Open protocol (no vendor lock-in)
    • Speed
    • Change data capture
    • Time travel
  5. While Parquet also provides columnar storage and has efficient read/write operations, its lack of ACID transaction capabilities distinguishes Delta Lake.
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